An algorithm for moving between adjacent regions in a binary digital image representated by a bintree is presented. This algorithm differs from other neighbour-finding algorithms in hierarchical image representations, as it exploits the nature of bintrees and thus can perform independently of any specific dimension of image or space. The algorithm is hybrid in its nature, as it uses a linear tree notation (locational codes) to find its way in a tree implemented with pointers.
Die Umstellung der Softwarentwicklung auf ein neues Paradigma ist kein einfaches Unterfangen. Es muss in diesem Rahmen nicht nur eine neue Programmiersprache, sondern vor allem ein neuer Denkansatz eingeführt werden.
Wir beschreiben in diesem Artikel unser Konzept für die Schulung ganzer Entwicklungsteams, die mit objektorientierter Technologie arbeiten wollen. Wir werden auch die Erfahrungen darstellen, die ein Geschäftsbereich der Ascom mit dieser Umschulung gemacht hat.
This paper describes the outline of our lecture and the experience we have had when introducing object--oriented programming, design, and software architecture to students of different educational and vocational backgrounds.
While other courses on object--oriented programming only show how to implement things in an object--oriented way, we emphasize on the production of reusable class libraries and frameworks.
Object-oriented analysis, design, and programming is a software development technology which has attracted universal attention in the past few year. We do not think that object-oriented technology is a completely new approach to software construction, it is merely the consequent continuation of software engineering principles which evolved since 1968, the year of birth of software engineering. This paper describes a course on software engineering with objects which tracks the evolution of this discipline. We work through the history of these concepts using a single application domain, demonstrating how the relevant analysis and design methods evolved over time, culminating in object-oriented techniques.
Generalized digital images, subsequently called hyperimages, represent a variation of the conventional digital images which implies pixels of different dimensions within the same image. The extent of a hyperimage is the disjoint union of all pixel extents it contains, which are relatively open unit cubes with respect to the euclidean topology of the underlying space. This approach is independent of any specific dimension of image and space, respectively, and allows strict partitioning of images into subimages, not just subdividing.
Since the storage required by a $d$-dimensional hyperimage of resolution $n^d$ is $\approx 2^{d}n^{d}$ when using a binary matrix representation, a more space efficient bintree representation is investigated. Algorithms for the Boolean operations, the computation of elementary topological properties and the computation of some important measures of $d$-dimensional hyperimages (volume, surface, Euler characteristic) are presented. Because of the nature of bintrees, the implementation of these algorithms, too, can be performed independently of any specific dimension of image and space.
This book describes the implemenation of a statistics package with numerically very robust algorithms. This package has been used for long years for the education of students at the Dept. of Mathematical Statistics of the University of Bern.